Dissociating respiratory depression and analgesia via a data-driven model of interacting respiratory and pain networks

通过呼吸和疼痛网络相互作用的数据驱动模型分离呼吸抑制和镇痛

• 批准号: 10644300
• 负责人: Ryan Sean Phillips
• 金额: $ 10.68万
• 依托单位: SEATTLE CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10644300
• 关键词: Absence of pain sensation; Address; Affect; Analgesics; Apnea; Attenuated; Automobile Driving; Award; Brain Stem; Breathing; Career Choice; Cause of Death; Cell Nucleus; Cells; Cessation of life; Clinical; Complex; Computer Models; Coupled; Dangerousness; Data; Data Set; Development; Disparate; Dissociation; Frequencies; Generations; Genes; Genetic; Health; Injections; Interruption; Investigation; Life; Link; Mediating; Mentored Research Scientist Development Award; Microinjections; Modeling; Molecular Target; Neurobiology; Neurons; Opiate Addiction; Opioid; Opioid Receptor; Overdose; Pain; Pain Clinics; Pain management; Pattern; Play; Population; Productivity; Property; Public Health; Recording of previous events; Regulation; Research; Respiration; Respiratory Center; Respiratory physiology; Role; Shapes; Site; Testing; Therapeutic; Training; Update; Ventilatory Depression; Viral; addiction; antagonist; behavioral study; career; cell type; cost; cost effective; data-driven model; improved; in vivo; in vivo evaluation; insight; neural circuit; novel; opioid epidemic; opioid injection; opioid mortality; optogenetics; pain perception; pain relief; parabrachial nucleus; pharmacologic; preBotzinger complex; prevent; receptor; recombinase; respiratory; response; side effect; therapeutic target

PROJECT SUMMARY This mentored research scientist development award will allow the candidate, Dr. Ryan Phillips, to establish an independent research career focused on understanding the complex circuit interactions that link pain and respiration. The training plan outlined in this award, combined with the candidate's background in brainstem pain circuits and computational modelling, make him ideally suited to successfully follow this career path. Opioid receptors are expressed in multiple brainstem regions that regulate pain and respiration, and the link between opioid-induced analgesia and respiratory depression poses a major clinical challenge. Indeed, the often difficult to predict side-effect of opioid-induced respiratory depression (OIRD) is the leading cause of opioid-related deaths driving the current opioid epidemic. Despite this public health significance, the mechanisms underlying OIRD are not fully understood and therapeutic strategies to prevent or reverse OIRD while maintaining analgesia are limited. A natural idea is that OIRD results from direct effects of opioids on respiratory rhythm-generating neurons of the pre-Bötzinger Complex (preBötC) in the ventral medulla. Although some preBötC neurons do express µ-opioid receptors (µORs), encoded by the Oprm1 gene, systemic opioids continue to produce respiratory depression and apneas even after genetic deletion of µORs in this population. Importantly, the contributions other µORs/Oprm1 expressing respiratory- and pain-related neurocircuitries to OIRD have been described. These brainstem regions, including the parabrachial (PB), Kolliker Fuse (KF), and rostral ventromedial medulla (RVM), all have important roles in regulating the respiratory rhythm generated by the preBötC. However, how opioids alter the network dynamics of these interacting regions remains unknown. We hypothesize that interruption of opioid sensitive network interactions is a major mechanism underlying OIRD caused by systemic opioids. This hypothesis will be tested using computational modeling and experimental approaches performed in tandem. Computational modeling has played a significant role in advancing the field of respiratory neurobiology and is ideally suited for examining the implications of disparate datasets and for low-cost, high throughput testing of hypothesized network features. In addition to existing data, our model will be guided and constrained by new cell-type- and projection- specific experimental manipulations in vivo that will constitute a significant training aspect of this award. By combining these strategies, our data-driven computational model will yield novel insights into the network dynamics that underlie opioid-induced respiratory depression as well as potential therapeutic targets to generate pain relief without respiratory side effects.

项目摘要 这个指导研究科学家发展奖将允许候选人，博士瑞安菲利普斯，建立一个 独立的研究生涯专注于了解复杂的电路相互作用，链接疼痛和 呼吸这个奖项中概述的培训计划，结合候选人在脑干疼痛方面的背景 电路和计算建模，使他非常适合成功地遵循这一职业道路。阿片类药物 受体在多个脑干区域表达，调节疼痛和呼吸， 阿片类药物引起的镇痛和呼吸抑制构成了主要的临床挑战。事实上， 预测阿片类药物引起的呼吸抑制（OIRD）的副作用是阿片类药物相关的主要原因， 导致当前阿片类药物流行的死亡。尽管这一公共卫生意义重大， OIRD尚未完全了解，在维持镇痛的同时预防或逆转OIRD的治疗策略 是有限的。一个自然的想法是OIRD是阿片类药物对呼吸节律产生的直接影响的结果 腹侧延髓中的前Bötinger复合体（preBötC）神经元。尽管有些preBötC神经元 表达由Oprm 1基因编码的μ-阿片受体（μ OR），系统性阿片类药物继续产生 呼吸抑制和呼吸暂停，即使在该人群中基因缺失µ OR后。重要的是 其他表达呼吸和疼痛相关神经回路的µ OR/Oprm 1对OIRD的贡献已经被证实， 介绍了这些脑干区域，包括臂旁（PB）、Kolliker脑干（KF）和头端腹内侧区， 延髓（RVM），都有重要的作用，在调节呼吸节律所产生的preBötC。然而，在这方面， 阿片类药物如何改变这些相互作用区域的网络动力学仍然是未知的。我们假设 阿片类药物敏感网络相互作用的中断是OIRD的主要机制， 阿片类药物这一假设将使用计算建模和实验方法进行测试 前后呼应计算建模在推进呼吸系统领域中发挥了重要作用。 神经生物学，非常适合检查不同数据集的影响，并为低成本，高 假设的网络特征的吞吐量测试。除了现有的数据，我们的模型将被引导， 受体内新的细胞类型和投射特异性实验操作的限制， 这是该奖项的重要组成部分。通过结合这些策略，我们的数据驱动计算模型将 对阿片类药物诱导的呼吸抑制以及 潜在的治疗靶点，以产生疼痛缓解而没有呼吸副作用。